Customer Churn Prediction

Loading libraries and datasets

In [8]:
import pandas as pd
import numpy as np
import missingno as msno
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier
from sklearn import metrics
from sklearn.metrics import roc_curve
from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, classification_report
dataset= pd.read_csv(r'C:\Users\Balaji\Downloads\Telco-Customer-Churn.csv')
dataset.head()
Out[8]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.5 No
2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes

5 rows × 21 columns

In [9]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB
In [10]:
dataset.columns.values
Out[10]:
array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',
       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',
       'TotalCharges', 'Churn'], dtype=object)
In [9]:
dataset.dtypes
Out[9]:
customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges         object
Churn                object
dtype: object

Visualizing the missing values

In [10]:
msno.matrix(dataset)
Out[10]:
<Axes: >

Data Manipulation

In [11]:
dataset=dataset.drop(['customerID'],axis=1)
dataset.head()
Out[11]:
gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 Female 0 Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 Male 0 No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.5 No
2 Male 0 No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 Male 0 No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 Female 0 No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
In [11]:
dataset['TotalCharges']=pd.to_numeric(dataset.TotalCharges,errors='coerce')
dataset.isnull().sum()
Out[11]:
customerID           0
gender               0
SeniorCitizen        0
Partner              0
Dependents           0
tenure               0
PhoneService         0
MultipleLines        0
InternetService      0
OnlineSecurity       0
OnlineBackup         0
DeviceProtection     0
TechSupport          0
StreamingTV          0
StreamingMovies      0
Contract             0
PaperlessBilling     0
PaymentMethod        0
MonthlyCharges       0
TotalCharges        11
Churn                0
dtype: int64
In [14]:
dataset[np.isnan(dataset['TotalCharges'])]
Out[14]:
gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
488 Female 0 Yes Yes 0 No No phone service DSL Yes No Yes Yes Yes No Two year Yes Bank transfer (automatic) 52.55 NaN No
753 Male 0 No Yes 0 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service Two year No Mailed check 20.25 NaN No
936 Female 0 Yes Yes 0 Yes No DSL Yes Yes Yes No Yes Yes Two year No Mailed check 80.85 NaN No
1082 Male 0 Yes Yes 0 Yes Yes No No internet service No internet service No internet service No internet service No internet service No internet service Two year No Mailed check 25.75 NaN No
1340 Female 0 Yes Yes 0 No No phone service DSL Yes Yes Yes Yes Yes No Two year No Credit card (automatic) 56.05 NaN No
3331 Male 0 Yes Yes 0 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service Two year No Mailed check 19.85 NaN No
3826 Male 0 Yes Yes 0 Yes Yes No No internet service No internet service No internet service No internet service No internet service No internet service Two year No Mailed check 25.35 NaN No
4380 Female 0 Yes Yes 0 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service Two year No Mailed check 20.00 NaN No
5218 Male 0 Yes Yes 0 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service One year Yes Mailed check 19.70 NaN No
6670 Female 0 Yes Yes 0 Yes Yes DSL No Yes Yes Yes Yes No Two year No Mailed check 73.35 NaN No
6754 Male 0 No Yes 0 Yes Yes DSL Yes Yes No Yes No No Two year Yes Bank transfer (automatic) 61.90 NaN No
In [12]:
dataset.drop(labels=dataset[dataset['tenure']==0].index, axis=0, inplace=True)
dataset[dataset['tenure']==0].index
Out[12]:
Index([], dtype='int64')
In [7]:
dataset.isnull().sum()
Out[7]:
customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64
In [13]:
dataset["SeniorCitizen"]=dataset["SeniorCitizen"].map({0: "No", 1: "Yes"})
dataset.head()
Out[13]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female No Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male No No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.50 No
2 3668-QPYBK Male No No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male No No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female No No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes

5 rows × 21 columns

In [14]:
dataset["InternetService"].describe(include=['object','bool'])
Out[14]:
count            7032
unique              3
top       Fiber optic
freq             3096
Name: InternetService, dtype: object
In [10]:
numeric_col=['tenure', 'MonthlyCharges' , 'TotalCharges']
dataset[numeric_col].describe()
Out[10]:
tenure MonthlyCharges
count 7032.000000 7032.000000
mean 32.421786 64.798208
std 24.545260 30.085974
min 1.000000 18.250000
25% 9.000000 35.587500
50% 29.000000 70.350000
75% 55.000000 89.862500
max 72.000000 118.750000

Data Visualization

In [15]:
g_labels = ['Male', 'Female']
c_labels = ['No', 'Yes']
# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=g_labels, values=dataset['gender'].value_counts(), name="Gender"),
              1, 1)
fig.add_trace(go.Pie(labels=c_labels, values=dataset['Churn'].value_counts(), name="Churn"),
              1, 2)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent+name", textfont_size=16)

fig.update_layout(
    title_text="Gender and Churn Distributions",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='Gender', x=0.16, y=0.5, font_size=20, showarrow=False),
                 dict(text='Churn', x=0.84, y=0.5, font_size=20, showarrow=False)])
fig.show()
In [16]:
dataset["Churn"][dataset["Churn"]=="No"].groupby(by=dataset["gender"]).count()
Out[16]:
gender
Female    2544
Male      2619
Name: Churn, dtype: int64
In [17]:
dataset["Churn"][dataset["Churn"]=="Yes"].groupby(by=dataset["gender"]).count()
Out[17]:
gender
Female    939
Male      930
Name: Churn, dtype: int64
In [19]:
plt.figure(figsize=(6, 6))
labels =["Churn: Yes","Churn:No"]
values = [1869,5163]
labels_gender = ["F","M","F","M"]
sizes_gender = [939,930 , 2544,2619]
colors = ['#ff6666', '#66b3ff']
colors_gender = ['#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6']
explode = (0.3,0.3) 
explode_gender = (0.1,0.1,0.1,0.1)
textprops = {"fontsize":15}
#Plot
plt.pie(values, labels=labels,autopct='%1.1f%%',pctdistance=1.08, labeldistance=0.8,colors=colors, startangle=90,frame=True, explode=explode,radius=10, textprops =textprops, counterclock = True, )
plt.pie(sizes_gender,labels=labels_gender,colors=colors_gender,startangle=90, explode=explode_gender,radius=7, textprops =textprops, counterclock = True, )
#Draw circle
centre_circle = plt.Circle((0,0),5,color='black', fc='white',linewidth=0)
fig = plt.gcf()
fig.gca().add_artist(centre_circle)

plt.title('Churn Distribution with respect to Gender: Male(M), Female(F)', fontsize=15, y=1.1)

# show plotted graph
plt.axis('equal')
plt.tight_layout()
plt.show()
In [20]:
fig = px.histogram(dataset, x="Churn", color="Contract", barmode="group", title="<b>Customer contract distribution<b>")
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [21]:
labels = dataset['PaymentMethod'].unique()
values = dataset['PaymentMethod'].value_counts()

fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.update_layout(title_text="<b>Payment Method Distribution</b>")
fig.show()
In [87]:
fig = px.histogram(dataset, x="Churn", color="PaymentMethod", title="<b>Customer Payment Method distribution with respect to Churn</b>")
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [23]:
dataset["InternetService"].unique()
Out[23]:
array(['DSL', 'Fiber optic', 'No'], dtype=object)
In [24]:
 dataset[dataset["gender"]=="Male"][["InternetService", "Churn"]].value_counts()
Out[24]:
InternetService  Churn
DSL              No       992
Fiber optic      No       910
No               No       717
Fiber optic      Yes      633
DSL              Yes      240
No               Yes       57
Name: count, dtype: int64
In [25]:
dataset[dataset["gender"]=="Female"][["InternetService", "Churn"]].value_counts()
Out[25]:
InternetService  Churn
DSL              No       965
Fiber optic      No       889
No               No       690
Fiber optic      Yes      664
DSL              Yes      219
No               Yes       56
Name: count, dtype: int64
In [86]:
fig = go.Figure()

fig.add_trace(go.Bar(
  x = [['Churn:No', 'Churn:No', 'Churn:Yes', 'Churn:Yes'],
       ["Female", "Male", "Female", "Male"]],
  y = [965, 992, 219, 240],
  name = 'DSL',
))

fig.add_trace(go.Bar(
  x = [['Churn:No', 'Churn:No', 'Churn:Yes', 'Churn:Yes'],
       ["Female", "Male", "Female", "Male"]],
  y = [889, 910, 664, 633],
  name = 'Fiber optic',
))

fig.add_trace(go.Bar(
  x = [['Churn:No', 'Churn:No', 'Churn:Yes', 'Churn:Yes'],
       ["Female", "Male", "Female", "Male"]],
  y = [690, 717, 56, 57],
  name = 'No Internet',
))
fig.update_layout(title_text="<b>Churn Distribution with respect to Internet Service and Gender</b>")

fig.show()
In [91]:
color_map = {"Yes": "#FF97FF", "No": "#AB63FA"}
fig = px.histogram(dataset, x="Churn", color="Dependents", barmode="group", title="<b>Dependents distribution</b>", color_discrete_map=color_map)
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [92]:
color_map = {"Yes": '#FFA15A', "No": '#00CC96'}
fig = px.histogram(dataset, x="Churn", color="Partner", barmode="group", title="<b>Chrun distribution with respect to Partners</b>", color_discrete_map=color_map)
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [93]:
color_map = {"Yes": '#00CC96', "No": '#B6E880'}
fig = px.histogram(dataset, x="Churn", color="SeniorCitizen", title="<b>Chrun distribution with respect to Senior Citizen</b>", color_discrete_map=color_map)
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [94]:
color_map = {"Yes": "#FF97FF", "No": "#AB63FA"}
fig = px.histogram(dataset, x="Churn", color="OnlineSecurity", barmode="group", title="<b>Churn with respect to Online Security</b>", color_discrete_map=color_map)
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [95]:
color_map = {"Yes": '#FFA15A', "No": '#00CC96'}
fig = px.histogram(dataset, x="Churn", color="PaperlessBilling",  title="<b>Chrun distribution with respect to Paperless Billing</b>", color_discrete_map=color_map)
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [33]:
fig = px.histogram(dataset, x="Churn", color="TechSupport",barmode="group",  title="<b>Chrun distribution with respect to TechSupport</b>")
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [96]:
color_map = {"Yes": '#00CC96', "No": '#B6E880'}
fig = px.histogram(dataset, x="Churn", color="PhoneService", title="<b>Chrun distribution with respect to Phone Service</b>", color_discrete_map=color_map)
fig.update_layout(width=700, height=500, bargap=0.1)
fig.show()
In [37]:
sns.set_context("paper",font_scale=1.1)
ax = sns.kdeplot(dataset.MonthlyCharges[(dataset["Churn"] == 'No') ],
                color="Red", shade = True);
ax = sns.kdeplot(dataset.MonthlyCharges[(dataset["Churn"] == 'Yes') ],
                ax =ax, color="Blue", shade= True);
ax.legend(["Not Churn","Churn"],loc='upper right');
ax.set_ylabel('Density');
ax.set_xlabel('Monthly Charges');
ax.set_title('Distribution of monthly charges by churn');
In [42]:
fig = px.box(dataset, x='Churn', y = 'tenure')

# Update yaxis properties
fig.update_yaxes(title_text='Tenure (Months)', row=1, col=1)
# Update xaxis properties
fig.update_xaxes(title_text='Churn', row=1, col=1)

# Update size and title
fig.update_layout(autosize=True, width=750, height=600,
    title_font=dict(size=25, family='Courier'),
    title='<b>Tenure vs Churn</b>',
)

fig.show()
In [44]:
plt.figure(figsize=(25, 10))

corr = dataset.apply(lambda x: pd.factorize(x)[0]).corr()

mask = np.triu(np.ones_like(corr, dtype=bool))

ax = sns.heatmap(corr, mask=mask, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, linewidths=.2, cmap='coolwarm', vmin=-1, vmax=1)

Data Preprocessing

In [46]:
def object_to_int(dataframe_series):
    if dataframe_series.dtype=='object':
        dataframe_series = LabelEncoder().fit_transform(dataframe_series)
    return dataframe_series
dataset= dataset.apply(lambda x: object_to_int(x))
dataset.head()
Out[46]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 5365 0 0 1 0 1 0 1 0 0 ... 0 0 0 0 0 1 2 29.85 2504 0
1 3953 1 0 0 0 34 1 0 0 2 ... 2 0 0 0 1 0 3 56.95 1465 0
2 2558 1 0 0 0 2 1 0 0 2 ... 0 0 0 0 0 1 3 53.85 156 1
3 5524 1 0 0 0 45 0 1 0 2 ... 2 2 0 0 1 0 0 42.30 1399 0
4 6500 0 0 0 0 2 1 0 1 0 ... 0 0 0 0 0 1 2 70.70 924 1

5 rows × 21 columns

In [47]:
plt.figure(figsize=(14,7))
dataset.corr()['Churn'].sort_values(ascending = False)
Out[47]:
Churn               1.000000
MonthlyCharges      0.192858
PaperlessBilling    0.191454
SeniorCitizen       0.150541
PaymentMethod       0.107852
MultipleLines       0.038043
TotalCharges        0.012891
PhoneService        0.011691
gender             -0.008545
customerID         -0.017858
StreamingTV        -0.036303
StreamingMovies    -0.038802
InternetService    -0.047097
Partner            -0.149982
Dependents         -0.163128
DeviceProtection   -0.177883
OnlineBackup       -0.195290
TechSupport        -0.282232
OnlineSecurity     -0.289050
tenure             -0.354049
Contract           -0.396150
Name: Churn, dtype: float64
<Figure size 1400x700 with 0 Axes>
In [49]:
X = dataset.drop(columns = ['Churn'])
y = dataset['Churn'].values
In [50]:
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.30, random_state = 40, stratify=y)
In [51]:
def distplot(feature, frame, color='r'):
    plt.figure(figsize=(8,3))
    plt.title("Distribution for {}".format(feature))
    ax = sns.distplot(frame[feature], color= color)
num_cols = ["tenure", 'MonthlyCharges', 'TotalCharges']
for feat in num_cols: distplot(feat, dataset)
In [56]:
df_std = pd.DataFrame(StandardScaler().fit_transform(dataset[num_cols].astype('float64')),
                       columns=numeric_col)
for feat in numeric_col: distplot(feat, df_std, color='c')
In [57]:
# Divide the columns into 3 categories, one ofor standardisation, one for label encoding and one for one hot encoding

cat_cols_ohe =['PaymentMethod', 'Contract', 'InternetService'] # those that need one-hot encoding
cat_cols_le = list(set(X_train.columns)- set(num_cols) - set(cat_cols_ohe)) #those that need label encoding
scaler= StandardScaler()

X_train[numeric_col] = scaler.fit_transform(X_train[num_cols])
X_test[numeric_col] = scaler.transform(X_test[num_cols])

Machine Learning Model Evaluations and Predictions

K-Nearest Neighbour

In [58]:
knn_model = KNeighborsClassifier(n_neighbors = 11) 
knn_model.fit(X_train,y_train)
predicted_y = knn_model.predict(X_test)
accuracy_knn = knn_model.score(X_test,y_test)
print("KNN accuracy:",accuracy_knn)

KNN accuracy: 0.7232227488151659
In [59]:
print(classification_report(y_test, predicted_y))

              precision    recall  f1-score   support

           0       0.74      0.96      0.84      1549
           1       0.38      0.07      0.11       561

    accuracy                           0.72      2110
   macro avg       0.56      0.51      0.47      2110
weighted avg       0.64      0.72      0.64      2110

Support Vector Classifier

In [64]:
svc_model = SVC(random_state = 1)
svc_model.fit(X_train,y_train)
predict_y = svc_model.predict(X_test)
accuracy_svc = svc_model.score(X_test,y_test)
print("SVM accuracy is :",accuracy_svc)

SVM accuracy is : 0.7341232227488151
In [63]:
print(classification_report(y_test, predict_y))

              precision    recall  f1-score   support

           0       0.73      1.00      0.85      1549
           1       0.00      0.00      0.00       561

    accuracy                           0.73      2110
   macro avg       0.37      0.50      0.42      2110
weighted avg       0.54      0.73      0.62      2110

Random Forest

In [99]:
model_rf = RandomForestClassifier(n_estimators=500 , oob_score = True, n_jobs = -1,
                                  random_state =50, max_features = "auto",
                                  max_leaf_nodes = 30)
model_rf.fit(X_train, y_train)

prediction_test = model_rf.predict(X_test)
print (metrics.accuracy_score(y_test, prediction_test))

0.8118483412322275
In [66]:
print(classification_report(y_test, prediction_test))

              precision    recall  f1-score   support

           0       0.83      0.93      0.88      1549
           1       0.72      0.48      0.58       561

    accuracy                           0.81      2110
   macro avg       0.77      0.71      0.73      2110
weighted avg       0.80      0.81      0.80      2110
In [67]:
plt.figure(figsize=(4,3))
sns.heatmap(confusion_matrix(y_test, prediction_test),
                annot=True,fmt = "d",linecolor="k",linewidths=3)
    
plt.title(" RANDOM FOREST CONFUSION MATRIX",fontsize=14)
plt.show()
In [68]:
y_rfpred_prob = model_rf.predict_proba(X_test)[:,1]
fpr_rf, tpr_rf, thresholds = roc_curve(y_test, y_rfpred_prob)
plt.plot([0, 1], [0, 1], 'k--' )
plt.plot(fpr_rf, tpr_rf, label='Random Forest',color = "r")
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Random Forest ROC Curve',fontsize=16)
plt.show();

Logistic Regression

lr_model = LogisticRegression() lr_model.fit(X_train,y_train) accuracy_lr = lr_model.score(X_test,y_test) print("Logistic Regression accuracy is :",accuracy_lr)

In [70]:
lr_pred= lr_model.predict(X_test)
report = classification_report(y_test,lr_pred)
print(report)

              precision    recall  f1-score   support

           0       0.85      0.91      0.88      1549
           1       0.68      0.56      0.61       561

    accuracy                           0.81      2110
   macro avg       0.77      0.73      0.75      2110
weighted avg       0.81      0.81      0.81      2110
In [71]:
plt.figure(figsize=(4,3))
sns.heatmap(confusion_matrix(y_test, lr_pred),
                annot=True,fmt = "d",linecolor="k",linewidths=3)
    
plt.title("LOGISTIC REGRESSION CONFUSION MATRIX",fontsize=14)
plt.show()
In [72]:
y_pred_prob = lr_model.predict_proba(X_test)[:,1]
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
plt.plot([0, 1], [0, 1], 'k--' )
plt.plot(fpr, tpr, label='Logistic Regression',color = "r")
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Logistic Regression ROC Curve',fontsize=16)
plt.show();

Decision Tree Classifier

In [73]:
dt_model = DecisionTreeClassifier()
dt_model.fit(X_train,y_train)
predictdt_y = dt_model.predict(X_test)
accuracy_dt = dt_model.score(X_test,y_test)
print("Decision Tree accuracy is :",accuracy_dt)

Decision Tree accuracy is : 0.7322274881516587
In [74]:
print(classification_report(y_test, predictdt_y))

              precision    recall  f1-score   support

           0       0.83      0.80      0.82      1549
           1       0.50      0.53      0.52       561

    accuracy                           0.73      2110
   macro avg       0.66      0.67      0.67      2110
weighted avg       0.74      0.73      0.74      2110

AdaBoost Classifier

In [75]:
a_model = AdaBoostClassifier()
a_model.fit(X_train,y_train)
a_preds = a_model.predict(X_test)
print("AdaBoost Classifier accuracy")
metrics.accuracy_score(y_test, a_preds)

AdaBoost Classifier accuracy
Out[75]:
0.8080568720379147
In [76]:
print(classification_report(y_test, a_preds))

              precision    recall  f1-score   support

           0       0.84      0.90      0.87      1549
           1       0.67      0.54      0.60       561

    accuracy                           0.81      2110
   macro avg       0.76      0.72      0.74      2110
weighted avg       0.80      0.81      0.80      2110
In [98]:
plt.figure(figsize=(4,3))
sns.heatmap(confusion_matrix(y_test, a_preds), annot=True,fmt = "d",linecolor="k",linewidths=3)
    
plt.title("AdaBoost Classifier Confusion Matrix",fontsize=14)
plt.show()

Gradient Boosting Classifier

In [78]:
gb = GradientBoostingClassifier()
gb.fit(X_train, y_train)
gb_pred = gb.predict(X_test)
print("Gradient Boosting Classifier", accuracy_score(y_test, gb_pred))

Gradient Boosting Classifier 0.8090047393364929
In [79]:
print(classification_report(y_test, gb_pred))

              precision    recall  f1-score   support

           0       0.85      0.90      0.87      1549
           1       0.67      0.55      0.60       561

    accuracy                           0.81      2110
   macro avg       0.76      0.73      0.74      2110
weighted avg       0.80      0.81      0.80      2110
In [80]:
plt.figure(figsize=(4,3))
sns.heatmap(confusion_matrix(y_test, gb_pred),
                annot=True,fmt = "d",linecolor="k",linewidths=3)
    
plt.title("Gradient Boosting Classifier Confusion Matrix",fontsize=14)
plt.show()

Voting classifier

In [81]:
from sklearn.ensemble import VotingClassifier
clf1 = GradientBoostingClassifier()
clf2 = LogisticRegression()
clf3 = AdaBoostClassifier()
eclf1 = VotingClassifier(estimators=[('gbc', clf1), ('lr', clf2), ('abc', clf3)], voting='soft')
eclf1.fit(X_train, y_train)
predictions = eclf1.predict(X_test)
print("Final Accuracy Score ")
print(accuracy_score(y_test, predictions))

Final Accuracy Score 
0.814218009478673
In [82]:
print(classification_report(y_test, predictions))

              precision    recall  f1-score   support

           0       0.85      0.90      0.88      1549
           1       0.68      0.57      0.62       561

    accuracy                           0.81      2110
   macro avg       0.77      0.74      0.75      2110
weighted avg       0.81      0.81      0.81      2110
In [97]:
plt.figure(figsize=(4,3))
sns.heatmap(confusion_matrix(y_test, predictions), annot=True,fmt = "d",linecolor="k",linewidths=3)
    
plt.title("FINAL CONFUSION MATRIX",fontsize=14)
plt.show()
In [ ]: